interfaces.nipy.utils¶
Similarity¶
Calculates similarity between two 3D volumes. Both volumes have to be in the same coordinate system, same space within that coordinate system and with the same voxel dimensions.
Deprecated since version 0.10.0: Use nipype.algorithms.metrics.Similarity
instead.
Example¶
>>> from nipype.interfaces.nipy.utils import Similarity
>>> similarity = Similarity()
>>> similarity.inputs.volume1 = 'rc1s1.nii'
>>> similarity.inputs.volume2 = 'rc1s2.nii'
>>> similarity.inputs.mask1 = 'mask.nii'
>>> similarity.inputs.mask2 = 'mask.nii'
>>> similarity.inputs.metric = 'cr'
>>> res = similarity.run() # doctest: +SKIP
Inputs:
[Mandatory]
volume1: (a pathlike object or string representing an existing file)
3D volume
volume2: (a pathlike object or string representing an existing file)
3D volume
[Optional]
mask1: (a pathlike object or string representing an existing file)
3D volume
mask2: (a pathlike object or string representing an existing file)
3D volume
metric: ('cc' or 'cr' or 'crl1' or 'mi' or 'nmi' or 'slr' or a
callable value, nipype default value: None)
str or callable
Cost-function for assessing image similarity. If a string,
one of 'cc': correlation coefficient, 'cr': correlation
ratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual
information, 'nmi': normalized mutual information, 'slr':
supervised log-likelihood ratio. If a callable, it should
take a two-dimensional array representing the image joint
histogram as an input and return a float.
Outputs:
similarity: (a float)
Similarity between volume 1 and 2